Notebooks Are McDonalds of Code

What people use notebooks for

• Widely used for data science, statistics, ML, and scientific/engineering work.
• Seen as digital lab notebooks: experiments, parameter sweeps, plots, and narrative in one place.
• Popular for teaching, documentation, reproducible reports, and sharing investigations.
• Sometimes used for debugging and incident response by attaching to live systems.
• A few mention serious production workflows and even mission‑critical systems built around notebooks.

Perceived advantages

• Fast feedback loop: tweak code or parameters and rerun a cell without restarting everything.
• Acts like a REPL plus documentation: code + markdown + rich media in one linear narrative.
• Inline tables/plots and layout near the code that produced them aid intuition and “feel” for data.
• In‑memory “caching” of expensive steps (large datasets, slow APIs, big models) avoids repeated load.
• Easy access to remote/cloud compute and data; many orgs expose only a Jupyter-like interface.
• Low barrier to entry for non‑software engineers and beginners.

Major criticisms

• Non‑linear, mutable state: hard to know what actually ran; easy to get irreproducible results.
• Encourages “spaghetti” and throwaway code that accidentally ends up in long‑lived use.
• Browser-based UX can feel sluggish and less interactive than native tools; plotting backends limited.
• Poor fit for long‑running, large‑scale pipelines; Makefiles/scripts seen as more robust.
• JSON format and saved outputs complicate version control and reviews.
• Some argue they’re misused for exploration; better to explore in an editor + REPL, and use notebooks only to present final analyses.

Production vs exploratory use

• Many insist notebooks should never be deployed to production; at most they prototype or document.
• Others report that real production systems and complex ML workflows are built on notebooks and can be maintained with discipline.
• Tension between software‑engineering ideals (tests, modularity, review) and scientists’ need for rapid, informal experimentation.

Alternatives and hybrids

• Scripts plus editor “cells” (# %% style) sent to a REPL to mimic notebook ergonomics while keeping plain text.
• Literate programming tools (org‑mode, RMarkdown/Quarto, similar systems) to combine narrative and code.
• Practice of moving stable logic into modules/packages and keeping notebooks as a thin exploratory or presentation layer.